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Greetings, explorer. I am Pratyush Pratim Thakur, an AI/ML developer focused on computer vision, deep learning architectures, and scalable machine intelligence systems. Currently pursuing a B.Tech in CSE (Artificial Intelligence & Machine Learning), I build real-world computer vision pipelines, train deep learning networks, and construct MLOps frameworks to deploy models efficiently. My mission is to develop robust algorithms that can perceive, learn, and scale.
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[ View Plaintext Calibration Data ]
| PYTHON CORE | ███████████████████░ 95% |
| TENSORFLOW | ████████████████░░░░ 80% |
| OPENCV VISION | ██████████████████░░ 90% |
| MACHINE LEARNING | ██████████████████░░ 90% |
| DEEP LEARNING | ███████████████░░░░░ 75% |
[ View Technical Specifications for Projects ]
| DEPLOYMENT_ID | ARCHITECTURAL_PARADIGM | METRICS_STATUS |
|---|---|---|
| 01 // SURVEILLANCE_AI | Background subtraction algorithms, frame differences, and contours detection linked to security triggers. | 98.4% Frame Recall |
| 02 // FORECAST_LSTM | Stacked LSTM networks combined with ARIMA models to analyze linear trends and non-linear patterns. | 0.024 RMSE Val |
| 03 // NEURAL_CLASSIFIER | Gradient Boosting Machines, Random Forests, and custom neural classifier pipelines. | 96.8% Accuracy |
| 04 // CLUSTER_ANALYTICS | K-Means clustering, DBSCAN algorithms, and t-SNE dimensionality reduction for data segmentation. | 0.74 Silhouette Score |
| 05 // ROBOTIC_ARM_AI | Computer Vision detection of objects with coordinate maps coupled to robotic arm actuators. | Real-time Kinematics |
| 06 // KRMU_AI_AGENT | Enterprise RAG pipeline, Groq Llama-3.3 LLM, vector semantic search, next.js UI, ElevenLabs TTS. | 50ms RAG Latency |
| 07 // SHOPPER_PREDICT | Random Forest and XGBoost classifiers predicting online shopper session purchase intent. | 89.2% Accuracy |
| 08 // URBAN_FORK | Immersive luxury fine dining web portal with Lenis scroll, GSAP orchestration, and NextJS 16. | 60fps+ Smooth Scroll |
[ Config: GitHub Actions Snake Generator ]
<p style="color: #00F7FF; margin-top: 10px; font-family: 'Courier New', monospace; font-size: 12px;">Copy the configuration below and save it as <code>.github/workflows/generate-snake.yml</code> to run the automated snake grid generator:</p>
name: Generate Snake Animation
on:
schedule: # Execute once every 24 hours
- cron: "0 0 * * *"
workflow_dispatch:
push:
branches:
- main
jobs:
generate:
runs-on: ubuntu-latest
timeout-minutes: 10
steps:
- name: Generate Contribution Snake
uses: Platane/snk/svg-only@v3
with:
github_user_name: pratyush-max
outputs: |
dist/github-contribution-grid-snake.svg
dist/github-contribution-grid-snake-dark.svg?palette=github-dark&color_snake=%2300F7FF&color_dots=%23161b22,%230e4429,%23006d32,%2326a641,%2339d353
- name: Push Snake SVG to Output Branch
uses: crazy-max/ghaction-github-pages@v5
with:
target_branch: output
build_dir: dist
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
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If you find my predictive models or vision matrices valuable, consider fueling my cognitive circuits with caffeine: 
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© 2026 PRATYUSH PRATIM THAKUR // SECURED BY QUANTUM_CRYPTO_CORE // ALL RIGHTS RESERVED